What your site is actually doing.
One page: what is working, what is costing you visitors, and what to fix next. A phone-and-desktop teardown of the live site: 4 phone loads + 2 desktop loads, cross-checked against real Chrome users from Google's field data. No login, no insider access, no Harvv pixel needed. The full evidence is at the bottom.
Working
What's already working. These held up across every load.
Speed is good.
Layout stays put as it loads.
No JavaScript errors on load.
Lighthouse scores SEO 100/100. The fundamentals Google looks for are present.
Nothing spilled past the edge at either 390px (phone) or 1366px (desktop), so the structure is responsive.
Costing you
Ranked by what hurts conversion most. Full evidence below.
8 form fields have no label
Meta Pixel (Facebook) is installed but misconfigured
Add quotations so AI engines cite this page
Google Analytics tracking broken
Page is heavy and slow on mobile data
Security headers are missing or weak
One focused change, on the pages people actually land on: Page is heavy and slow on mobile data. We re-measure the same samples after.
This is one of several AI-built sites we tore down the same way. The same friction repeats on Contractive, and we pulled the cross-site pattern together in the vibe-code friction tax.
01Findings, ranked by what hurts conversion most
| Severity | Finding | How we know |
|---|---|---|
| Medium | Page is heavy and slow on mobile dataBothPerformance Each visit downloads about 2.1 MB, roughly 974 KB of images and 1050 KB of JavaScript across 84 separate downloads. On a fast connection that's fine. On a phone with patchy mobile data, that's several seconds of blank screen before the page is readable. paste into Claude, Cursor, or ChatGPT | median across loads |
| Low | Unused JavaScript is being downloadedBothPerformance Code that never runs on this page still costs download and parse time on every visit. Splitting or removing it speeds up load. Lighthouse measured: Est savings of 478 KiB. paste into Claude, Cursor, or ChatGPT | |
| Low | Unused CSS is being downloadedBothPerformance Style rules that this page never uses still block rendering while they download. Trimming them frees the paint path. Lighthouse measured: Est savings of 52 KiB. paste into Claude, Cursor, or ChatGPT | |
| Low | The page loads from a lot of third-party servicesBoth Around 20 separate outside domains load on this page (analytics, ads, chat widgets, fonts). Each one is a connection that can be slow, fail, or change behavior outside your control. paste into Claude, Cursor, or ChatGPT | median across loads |
| High | 8 form fields have no labelBothAccessibility (WCAG)ConversionTracking Screen readers can't announce these fields, and a sighted user who clears the placeholder can't recover the prompt. Wrap each input in <label>…</label> or add aria-label. The exact elements we found: paste into Claude, Cursor, or ChatGPT | |
| High | Meta Pixel (Facebook) is installed but misconfiguredBothTracking Meta Pixel installed but no Lead/Purchase/Checkout event fires on this page, only PageView. Your Meta ad sessions can't be optimized for conversions until you track at least one downstream event. paste into Claude, Cursor, or ChatGPT | |
| High | Add quotations so AI engines cite this pageBothAI SearchSEO Generative engines (ChatGPT, Perplexity, AI Overviews) lift sourced, attributed quotes almost verbatim, and quotations are the single strongest citation lever (studies measure roughly +41%). Add 1-2 attributed expert quotes or blockquotes to the pages below. (Found across a sample of 8 pages from your sitemap, a partial crawl rather than your full site.) paste into Claude, Cursor, or ChatGPT | |
| High | Google Analytics tracking brokenBothTrackingConversion The Google Analytics request failed to complete on every one of the 4 test loads. If real visitors hit the same failure, GA is missing those visits and the dashboard has no way to flag it. Conversion numbers, audience counts, and channel attribution are all undercounting. Worth checking the tag loading order and any consent banner that might be blocking the request. paste into Claude, Cursor, or ChatGPT | |
| Low | No structured data for rich search resultsBoth The page has no schema.org markup. Adding the right type (Product, Article, Organization, FAQ) lets Google show rich results like star ratings and prices, which lift click-through for free. paste into Claude, Cursor, or ChatGPT | |
| Low | 4 potential dead-click targetsBothConversionAccessibility (WCAG)Tracking Elements styled like buttons but with no anchor, no <button> wrapper, no role="button", and no click attribute. Real visitors tap these expecting something to happen, then leave. Examples on this page: "People" (div.cat_tag), "Similar" (div.cat_tag), "Duplicates" (div.cat_tag). The exact elements we found: paste into Claude, Cursor, or ChatGPT | |
| Low | No llms.txt fileBothSEO No /llms.txt. Optional: some AI tools read this file (Anthropic, Vercel, Stripe, Cloudflare and Hugging Face publish one, and AI coding assistants use it to find docs), so adding one is a cheap courtesy to them. Google has said in writing that Google Search and its AI features do not use llms.txt, so do not expect a search-ranking or AI Overviews benefit from it. paste into Claude, Cursor, or ChatGPT | |
| Low | No Organization or WebSite schemaBothSEO No site-wide Organization or WebSite structured data found on any crawled page. This is the schema that tells Google and AI engines who you are (name, logo, social profiles) for the knowledge panel and brand recognition in AI answers. (Found across a sample of 8 pages from your sitemap, a partial crawl rather than your full site.) paste into Claude, Cursor, or ChatGPT | |
| Medium | Security headers are missing or weakBothSecurity The server response is missing browser-hardening headers that protect visitors and are a standard security and agency checklist item. Missing or weak here: Content-Security-Policy (the main defense against injected and cross-site scripts); clickjacking protection (X-Frame-Options or a CSP frame-ancestors rule); HSTS (Strict-Transport-Security, which forces HTTPS on return visits); X-Content-Type-Options: nosniff (stops MIME-type sniffing attacks). These are set at the server, CDN, or host level (most platforms expose them in settings or a config file) and do not change how the site looks or performs. paste into Claude, Cursor, or ChatGPT | |
| Medium | 6 interactive elements have no stable, accessible identityBothAccessibility (WCAG)Tracking These elements are clicked like buttons but expose no accessible name, or are a plain div/span used as a control with no role. Assistive tech announces only a role (or nothing), and analytics and heatmaps have no human-readable label or stable selector to bind the click to, so the click is both inaccessible and untrackable, and any redesign silently breaks click aggregation. Give each one a real <button>/<a>, an aria-label, and a stable id or data-attribute. The exact elements we found:
paste into Claude, Cursor, or ChatGPT | |
| Low | Some text is too small to read on phonesMobileAccessibility (WCAG)Conversion 8 chunks of text come in under 12 pixels on this page. Most visitors don't zoom, they just skim past anything that small. Bumping the smallest body text to 14 pixels makes the page read without effort. paste into Claude, Cursor, or ChatGPT | median across loads |
Accessibility findings are automated checks against Web Content Accessibility Guidelines (WCAG) 2.1 and 2.2. They flag potential barriers and legal risk, not a certification or a determination of compliance with the ADA, Section 508, or EN 301 549. Automated testing catches only a subset of issues; a full conformance review needs manual and assistive-technology testing by a qualified reviewer.
"How we know": unlabeled = a deterministic fact, identical on every load (e.g. element sizes). Most findings are this kind, so we only mark the exceptions: median across loads = a noisy lab metric, reported as a median. real-user field data = Google CrUX, actual Chrome visitors.
Structural and AI-search checks crawl up to 8 pages from your sitemap (a sample, not your full site). "Broken" means a link returned 404, 410, or 5xx, or did not respond; access-controlled pages (401, 403) are not counted.
02Performance: phone, desktop, and real visitors
| Metric | Mobile | Desktop | Read |
|---|---|---|---|
| TTFB (lab median) | 75 ms | 69 ms | Lab |
| FCP (lab median) | 134 ms | 158 ms | Lab |
| LCP (lab median) | 208 ms | 202 ms | Good |
| Page weight (median) | 2.1 MB | 3.2 MB | Watch |
| Real LCP (p75, url) | 1.8 s | Good | |
| Real INP (p75) | 89 ms | Good | |
| Real CLS (p75) | 0.00 | Good | |
Google Lighthouse (lab): Performance 56 mobile / 76 desktop, SEO 100, Accessibility 99, Best Practices 92.
Lab numbers are from a headless mobile browser on an unthrottled connection: treat them as a floor, not a typical experience.
03Tiny buttons are hard to tap on mobile
4 of 32 tappable items on this page come in below the platform minimums for reliable tapping on a phone (Apple recommends 44pt, Android 48dp; WCAG 2.5.8 sets 24px as the hard accessibility floor). The same ones came up small on every one of the 4 test loads, so this is the page itself, not a fluke.
The buttons measuring below the minimum on this scan:
- span 131x20 "upload an image."
- a 87x19 "Reversely.ai"
- a 170x20 "reersely-logo"
- a 92x20 "Reversely.ai"
The fix is CSS-only on most sites: add padding around the icon (don't just change the icon size) so the actual tap area is at least 44×44 pixels. No redesign, no new assets.
04Technical SEO & structured data
| Check | Result |
|---|---|
| Title | Reversely.ai - AI Reverse Image Search (38 chars) |
| Meta description | 141 chars |
| H1 | 1 on page |
| Canonical | Present |
| Structured data (JSON-LD) | None |
| Open Graph | Title + image |
05The fix checklist
Everything to fix, priority first, each tagged with the screen it affects and a rough effort. Work top to bottom.
- Page is heavy and slow on mobile dataBothSmall
- Unused JavaScript is being downloadedBothVaries
- Unused CSS is being downloadedBothVaries
- The page loads from a lot of third-party servicesBothDev afternoon
- 8 form fields have no labelBothVaries
- Meta Pixel (Facebook) is installed but misconfiguredBothVaries
- Add quotations so AI engines cite this pageBothVaries
- Google Analytics tracking brokenBothDev afternoon
- No structured data for rich search resultsBothVaries
- 4 potential dead-click targetsBothCSS only
- No llms.txt fileBothVaries
- No Organization or WebSite schemaBothVaries
- Security headers are missing or weakBothVaries
- 6 interactive elements have no stable, accessible identityBothDev afternoon
- Some text is too small to read on phonesMobileCSS only
Effort is a rough read from the outside: "CSS only" means no new assets or backend work, "1 line" means a single tag, "Dev afternoon" means a developer needs to touch tracking or scripts.
06What this report cannot tell you
Everything above is from the outside, looking at the page on a simulated phone and desktop. The questions that actually decide revenue need real visitors. Install the Harvv pixel (one script tag, 16 KB, zero personal data, no engineering project) and within about 72 hours you'd know which buttons real customers tapped and missed, how often Google Analytics is missing visits, and exactly where mobile shoppers stalled and left. This report shows you where to look. The pixel shows you how often it happens, and to whom.
Drop the Harvv pixel on reversely.ai and we turn this one-off scan into ongoing measured behavior: which taps miss, where sessions stall, and the real drop rates. Free to start, no card needed.
Add the pixel free07How we did this, and what it can't prove
- 4 mobile + 2 desktop loads of one URL from headless Chrome (iPhone viewport at 390px, desktop at 1366px), September 8, 2026. Enough loads to separate real defects from random noise, not a full-site crawl.
- Lab numbers, not real-user numbers, except the CrUX rows, which are real Chrome users. Real devices on real networks run slower.
- Friction is inferred, not counted. We can prove a button is small. We can't, from the outside, count how often it causes a missed tap. That requires the pixel on a live page.
About Harvv, the source of this teardown
Harvv is a behavioral UX analytics platform (harvv.com). A lightweight JavaScript pixel captures how real visitors behave on a site (dead clicks, rage clicks, scroll depth, Core Web Vitals, JavaScript errors, and 50+ other signals) and the engine turns them into prioritized, plain-English findings. This teardown is the outside-in version of that: the same detectors run against a public page, with no pixel installed.
How to read it. Every finding here is a reproducible, automated measurement, not an opinion: element sizes, contrast ratios, load metrics, and structured-data checks that anyone can re-run against the same URL. The method is stated in full above. Automated testing catches a subset of issues, so this is a starting point, not a certification.
Full disclosure. Harvv makes the pixel that would measure the friction these findings imply, so we have a commercial interest. That is exactly why the findings are kept to things a reader can verify independently, and why nothing here is inflated: an unreproducible claim would undermine the tool it is meant to demonstrate.
Prepared by Harvv (harvv.com), a behavioral UX analytics platform. Last updated September 8, 2026.